Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations881666
Missing cells30
Missing cells (%)< 0.1%
Duplicate rows440828
Duplicate rows (%)50.0%
Total size in memory80.7 MiB
Average record size in memory96.0 B

Variable types

Numeric8
Categorical4

Alerts

Dataset has 440828 (50.0%) duplicate rowsDuplicates
CustomerID is highly overall correlated with cancelouHigh correlation
cancelou is highly overall correlated with CustomerID and 1 other fieldsHigh correlation
ligacoes_callcenter is highly overall correlated with cancelouHigh correlation
ligacoes_callcenter has 139750 (15.9%) zerosZeros
dias_atraso has 33808 (3.8%) zerosZeros

Reproduction

Analysis started2025-11-21 00:14:53.701700
Analysis finished2025-11-21 00:15:30.958358
Duration37.26 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

High correlation 

Distinct440832
Distinct (%)50.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean225398.67
Minimum2
Maximum449999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:31.103526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile22050.15
Q1113621.75
median226125.5
Q3337739.25
95-th percentile425905.85
Maximum449999
Range449997
Interquartile range (IQR)224117.5

Descriptive statistics

Standard deviation129531.85
Coefficient of variation (CV)0.57467884
Kurtosis-1.2006439
Mean225398.67
Median Absolute Deviation (MAD)112049.5
Skewness-0.018485792
Sum1.9872589 × 1011
Variance1.6778499 × 1010
MonotonicityNot monotonic
2025-11-20T21:15:31.196354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4499992
 
< 0.1%
4492432
 
< 0.1%
4492422
 
< 0.1%
4492412
 
< 0.1%
4492402
 
< 0.1%
4492392
 
< 0.1%
4492382
 
< 0.1%
4492372
 
< 0.1%
4492362
 
< 0.1%
4492352
 
< 0.1%
Other values (440822)881644
> 99.9%
ValueCountFrequency (%)
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
82
< 0.1%
92
< 0.1%
102
< 0.1%
112
< 0.1%
122
< 0.1%
ValueCountFrequency (%)
4499992
< 0.1%
4499982
< 0.1%
4499972
< 0.1%
4499962
< 0.1%
4499952
< 0.1%
4499942
< 0.1%
4499932
< 0.1%
4499922
< 0.1%
4499912
< 0.1%
4499902
< 0.1%

idade
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39.373153
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:31.282599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median39
Q348
95-th percentile61
Maximum65
Range47
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.442362
Coefficient of variation (CV)0.31601133
Kurtosis-0.86485526
Mean39.373153
Median Absolute Deviation (MAD)9
Skewness0.1620154
Sum34713892
Variance154.81238
MonotonicityNot monotonic
2025-11-20T21:15:31.370686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
5027054
 
3.1%
4225156
 
2.9%
4024834
 
2.8%
4824758
 
2.8%
4724738
 
2.8%
4624736
 
2.8%
4424688
 
2.8%
4924662
 
2.8%
4124628
 
2.8%
4324596
 
2.8%
Other values (38)631814
71.7%
ValueCountFrequency (%)
1816438
1.9%
1916146
1.8%
2019106
2.2%
2119148
2.2%
2219278
2.2%
2319026
2.2%
2418930
2.1%
2519294
2.2%
2619384
2.2%
2718944
2.1%
ValueCountFrequency (%)
6510920
1.2%
6410992
1.2%
6311120
1.3%
6210576
1.2%
6110814
1.2%
6010860
1.2%
5911146
1.3%
5810746
1.2%
5710722
1.2%
5610954
1.2%

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size6.7 MiB
Male
500504 
Female
381160 

Length

Max length6
Median length4
Mean length4.8646378
Min length4

Characters and Unicode

Total characters4288976
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male500504
56.8%
Female381160
43.2%
(Missing)2
 
< 0.1%

Length

2025-11-20T21:15:31.462200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-20T21:15:31.531402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male500504
56.8%
female381160
43.2%

Most occurring characters

ValueCountFrequency (%)
e1262824
29.4%
a881664
20.6%
l881664
20.6%
M500504
 
11.7%
F381160
 
8.9%
m381160
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4288976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1262824
29.4%
a881664
20.6%
l881664
20.6%
M500504
 
11.7%
F381160
 
8.9%
m381160
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4288976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1262824
29.4%
a881664
20.6%
l881664
20.6%
M500504
 
11.7%
F381160
 
8.9%
m381160
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4288976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1262824
29.4%
a881664
20.6%
l881664
20.6%
M500504
 
11.7%
F381160
 
8.9%
m381160
 
8.9%

tempo_como_cliente
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean31.256312
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:31.596957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median32
Q346
95-th percentile58
Maximum60
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.255713
Coefficient of variation (CV)0.55207131
Kurtosis-1.1925219
Mean31.256312
Median Absolute Deviation (MAD)15
Skewness-0.061399502
Sum27557534
Variance297.75964
MonotonicityNot monotonic
2025-11-20T21:15:31.689871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3215656
 
1.8%
4915630
 
1.8%
5615624
 
1.8%
5515554
 
1.8%
3315540
 
1.8%
5215537
 
1.8%
3015500
 
1.8%
4715494
 
1.8%
4815474
 
1.8%
5715470
 
1.8%
Other values (50)726184
82.4%
ValueCountFrequency (%)
112814
1.5%
213150
1.5%
312834
1.5%
413212
1.5%
513338
1.5%
615408
1.7%
715138
1.7%
815340
1.7%
915068
1.7%
1015348
1.7%
ValueCountFrequency (%)
6015316
1.7%
5915194
1.7%
5815338
1.7%
5715470
1.8%
5615624
1.8%
5515554
1.8%
5415212
1.7%
5315330
1.7%
5215537
1.8%
5115188
1.7%

frequencia_uso
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.807496
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:31.769278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5862414
Coefficient of variation (CV)0.54317532
Kurtosis-1.1758168
Mean15.807496
Median Absolute Deviation (MAD)7
Skewness-0.043474089
Sum13936884
Variance73.723542
MonotonicityNot monotonic
2025-11-20T21:15:31.850594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1130622
 
3.5%
2930568
 
3.5%
2030516
 
3.5%
2530474
 
3.5%
3030464
 
3.5%
2130410
 
3.4%
1930408
 
3.4%
1230358
 
3.4%
2630268
 
3.4%
1530258
 
3.4%
Other values (20)577317
65.5%
ValueCountFrequency (%)
127594
3.1%
227266
3.1%
327686
3.1%
427098
3.1%
527432
3.1%
627492
3.1%
727110
3.1%
827450
3.1%
927540
3.1%
1030180
3.4%
ValueCountFrequency (%)
3030464
3.5%
2930568
3.5%
2830024
3.4%
2730242
3.4%
2630268
3.4%
2530474
3.5%
2430076
3.4%
2330144
3.4%
2230010
3.4%
2130410
3.4%

ligacoes_callcenter
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.6044366
Minimum0
Maximum10
Zeros139750
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:32.087226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0702161
Coefficient of variation (CV)0.85178808
Kurtosis-0.74591454
Mean3.6044366
Median Absolute Deviation (MAD)2
Skewness0.66680737
Sum3177902
Variance9.4262271
MonotonicityNot monotonic
2025-11-20T21:15:32.148235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0139750
15.9%
1138952
15.8%
2133142
15.1%
3105458
12.0%
477500
8.8%
549836
 
5.7%
1047800
 
5.4%
747740
 
5.4%
947260
 
5.4%
847118
 
5.3%
ValueCountFrequency (%)
0139750
15.9%
1138952
15.8%
2133142
15.1%
3105458
12.0%
477500
8.8%
549836
 
5.7%
647108
 
5.3%
747740
 
5.4%
847118
 
5.3%
947260
 
5.4%
ValueCountFrequency (%)
1047800
 
5.4%
947260
 
5.4%
847118
 
5.3%
747740
 
5.4%
647108
 
5.3%
549836
 
5.7%
477500
8.8%
3105458
12.0%
2133142
15.1%
1138952
15.8%

dias_atraso
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.965722
Minimum0
Maximum30
Zeros33808
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:32.216616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q319
95-th percentile28
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.2580578
Coefficient of variation (CV)0.63691463
Kurtosis-0.89567996
Mean12.965722
Median Absolute Deviation (MAD)6
Skewness0.26740667
Sum11431410
Variance68.195519
MonotonicityNot monotonic
2025-11-20T21:15:32.297260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1234396
 
3.9%
1134370
 
3.9%
2034350
 
3.9%
1334190
 
3.9%
1434156
 
3.9%
1034102
 
3.9%
734054
 
3.9%
1834054
 
3.9%
334050
 
3.9%
134042
 
3.9%
Other values (21)539900
61.2%
ValueCountFrequency (%)
033808
3.8%
134042
3.9%
233644
3.8%
334050
3.9%
433876
3.8%
533488
3.8%
633908
3.8%
734054
3.9%
833784
3.8%
933738
3.8%
ValueCountFrequency (%)
3017180
1.9%
2916892
1.9%
2816598
1.9%
2716356
1.9%
2616766
1.9%
2516724
1.9%
2416650
1.9%
2316646
1.9%
2216908
1.9%
2117340
2.0%

assinatura
Categorical

Distinct3
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size6.7 MiB
Standard
298255 
Premium
297354 
Basic
286052 

Length

Max length8
Median length7
Mean length6.6893942
Min length5

Characters and Unicode

Total characters5897778
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowBasic
3rd rowBasic
4th rowStandard
5th rowBasic

Common Values

ValueCountFrequency (%)
Standard298255
33.8%
Premium297354
33.7%
Basic286052
32.4%
(Missing)5
 
< 0.1%

Length

2025-11-20T21:15:32.392581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-20T21:15:32.448100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
standard298255
33.8%
premium297354
33.7%
basic286052
32.4%

Most occurring characters

ValueCountFrequency (%)
a882562
15.0%
d596510
10.1%
r595609
10.1%
m594708
10.1%
i583406
9.9%
S298255
 
5.1%
n298255
 
5.1%
t298255
 
5.1%
e297354
 
5.0%
P297354
 
5.0%
Other values (4)1155510
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5897778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a882562
15.0%
d596510
10.1%
r595609
10.1%
m594708
10.1%
i583406
9.9%
S298255
 
5.1%
n298255
 
5.1%
t298255
 
5.1%
e297354
 
5.0%
P297354
 
5.0%
Other values (4)1155510
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5897778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a882562
15.0%
d596510
10.1%
r595609
10.1%
m594708
10.1%
i583406
9.9%
S298255
 
5.1%
n298255
 
5.1%
t298255
 
5.1%
e297354
 
5.0%
P297354
 
5.0%
Other values (4)1155510
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5897778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a882562
15.0%
d596510
10.1%
r595609
10.1%
m594708
10.1%
i583406
9.9%
S298255
 
5.1%
n298255
 
5.1%
t298255
 
5.1%
e297354
 
5.0%
P297354
 
5.0%
Other values (4)1155510
19.6%

duracao_contrato
Categorical

Distinct3
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size6.7 MiB
Annual
354396 
Quarterly
353060 
Monthly
174207 

Length

Max length9
Median length7
Mean length7.3989325
Min length6

Characters and Unicode

Total characters6523365
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnnual
2nd rowMonthly
3rd rowQuarterly
4th rowMonthly
5th rowMonthly

Common Values

ValueCountFrequency (%)
Annual354396
40.2%
Quarterly353060
40.0%
Monthly174207
19.8%
(Missing)3
 
< 0.1%

Length

2025-11-20T21:15:32.514652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-20T21:15:32.566140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
annual354396
40.2%
quarterly353060
40.0%
monthly174207
19.8%

Most occurring characters

ValueCountFrequency (%)
n882999
13.5%
l881663
13.5%
u707456
10.8%
a707456
10.8%
r706120
10.8%
y527267
8.1%
t527267
8.1%
A354396
5.4%
Q353060
 
5.4%
e353060
 
5.4%
Other values (3)522621
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6523365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n882999
13.5%
l881663
13.5%
u707456
10.8%
a707456
10.8%
r706120
10.8%
y527267
8.1%
t527267
8.1%
A354396
5.4%
Q353060
 
5.4%
e353060
 
5.4%
Other values (3)522621
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6523365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n882999
13.5%
l881663
13.5%
u707456
10.8%
a707456
10.8%
r706120
10.8%
y527267
8.1%
t527267
8.1%
A354396
5.4%
Q353060
 
5.4%
e353060
 
5.4%
Other values (3)522621
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6523365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n882999
13.5%
l881663
13.5%
u707456
10.8%
a707456
10.8%
r706120
10.8%
y527267
8.1%
t527267
8.1%
A354396
5.4%
Q353060
 
5.4%
e353060
 
5.4%
Other values (3)522621
8.0%

total_gasto
Real number (ℝ)

Distinct68363
Distinct (%)7.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean631.61622
Minimum100
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:32.638160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile177
Q1480
median661
Q3830
95-th percentile965.8985
Maximum1000
Range900
Interquartile range (IQR)350

Descriptive statistics

Standard deviation240.80286
Coefficient of variation (CV)0.3812487
Kurtosis-0.75149138
Mean631.61622
Median Absolute Deviation (MAD)172.55
Skewness-0.4571733
Sum5.5687329 × 108
Variance57986.02
MonotonicityNot monotonic
2025-11-20T21:15:32.726806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234538
 
0.1%
432534
 
0.1%
703532
 
0.1%
139530
 
0.1%
845530
 
0.1%
581530
 
0.1%
133526
 
0.1%
534524
 
0.1%
613524
 
0.1%
269522
 
0.1%
Other values (68353)876374
99.4%
ValueCountFrequency (%)
100200
< 0.1%
100.022
 
< 0.1%
100.062
 
< 0.1%
100.072
 
< 0.1%
100.082
 
< 0.1%
100.094
 
< 0.1%
100.112
 
< 0.1%
100.124
 
< 0.1%
100.134
 
< 0.1%
100.166
 
< 0.1%
ValueCountFrequency (%)
1000222
< 0.1%
999.9910
 
< 0.1%
999.984
 
< 0.1%
999.976
 
< 0.1%
999.9614
 
< 0.1%
999.954
 
< 0.1%
999.9410
 
< 0.1%
999.938
 
< 0.1%
999.9212
 
< 0.1%
999.914
 
< 0.1%

meses_ultima_interacao
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.480868
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2025-11-20T21:15:32.810055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q322
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.5962028
Coefficient of variation (CV)0.59362483
Kurtosis-1.15376
Mean14.480868
Median Absolute Deviation (MAD)7
Skewness0.17677375
Sum12767260
Variance73.894703
MonotonicityNot monotonic
2025-11-20T21:15:32.882775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
733828
 
3.8%
1433542
 
3.8%
833524
 
3.8%
1533500
 
3.8%
633492
 
3.8%
133454
 
3.8%
1233444
 
3.8%
333422
 
3.8%
533420
 
3.8%
1033370
 
3.8%
Other values (20)546668
62.0%
ValueCountFrequency (%)
133454
3.8%
233326
3.8%
333422
3.8%
433140
3.8%
533420
3.8%
633492
3.8%
733828
3.8%
833524
3.8%
933064
3.8%
1033370
3.8%
ValueCountFrequency (%)
3025308
2.9%
2925134
2.9%
2825508
2.9%
2725574
2.9%
2625646
2.9%
2525206
2.9%
2425786
2.9%
2325288
2.9%
2225380
2.9%
2125290
2.9%

cancelou
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size6.7 MiB
1.0
499998 
0.0
381666 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2644992
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0499998
56.7%
0.0381666
43.3%
(Missing)2
 
< 0.1%

Length

2025-11-20T21:15:32.959484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-20T21:15:33.001394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0499998
56.7%
0.0381666
43.3%

Most occurring characters

ValueCountFrequency (%)
01263330
47.8%
.881664
33.3%
1499998
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2644992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01263330
47.8%
.881664
33.3%
1499998
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2644992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01263330
47.8%
.881664
33.3%
1499998
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2644992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01263330
47.8%
.881664
33.3%
1499998
 
18.9%

Interactions

2025-11-20T21:15:25.265838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:12.293330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:14.090318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:16.095413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:17.966024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:19.829219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:21.623539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:23.460585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:25.495291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:12.516330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:14.367241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:16.389118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:18.187737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:20.059166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:21.862772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:23.679588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:25.728329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:12.743418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:14.621350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:16.591760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:18.409283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:20.282110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:22.078082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:23.908023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:26.053300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:12.969899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:14.908812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:16.808852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:18.707276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:20.499124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:22.298810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:24.125940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:26.272523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:13.208949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:15.138598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:17.028444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:18.920844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:20.722899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:22.522262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:24.356603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:26.527723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:13.426334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:15.376037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:17.256905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:19.143706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:20.942680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:22.750391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:24.570555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:26.829667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:13.631856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:15.613936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:17.499418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:19.369061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:21.186634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:22.987939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:24.790369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:27.041097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:13.839465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:15.842170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:17.728863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:19.604304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:21.399081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:23.231529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-20T21:15:25.025027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-20T21:15:33.051311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CustomerIDassinaturacanceloudias_atrasoduracao_contratofrequencia_usoidadeligacoes_callcentermeses_ultima_interacaosexotempo_como_clientetotal_gasto
CustomerID1.0000.0140.949-0.2430.2910.038-0.163-0.470-0.1250.1660.0440.334
assinatura0.0141.0000.0210.0050.0070.0010.0070.0090.0030.0030.0390.008
cancelou0.9490.0211.0000.4020.4340.0590.4320.6150.1720.1750.0780.496
dias_atraso-0.2430.0050.4021.0000.112-0.0130.0510.1460.0390.063-0.015-0.104
duracao_contrato0.2910.0070.4340.1121.0000.0160.1220.1690.0470.0680.0230.136
frequencia_uso0.0380.0010.059-0.0130.0161.000-0.006-0.021-0.0050.013-0.0270.017
idade-0.1630.0070.4320.0510.122-0.0061.0000.1690.0260.066-0.010-0.070
ligacoes_callcenter-0.4700.0090.6150.1460.169-0.0210.1691.0000.0750.097-0.027-0.199
meses_ultima_interacao-0.1250.0030.1720.0390.047-0.0050.0260.0751.0000.156-0.007-0.052
sexo0.1660.0030.1750.0630.0680.0130.0660.0970.1561.0000.0110.077
tempo_como_cliente0.0440.0390.078-0.0150.023-0.027-0.010-0.027-0.0070.0111.0000.017
total_gasto0.3340.0080.496-0.1040.1360.017-0.070-0.199-0.0520.0770.0171.000

Missing values

2025-11-20T21:15:27.271005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-20T21:15:27.829499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-20T21:15:29.351433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIDidadesexotempo_como_clientefrequencia_usoligacoes_callcenterdias_atrasoassinaturaduracao_contratototal_gastomeses_ultima_interacaocancelou
02.030.0Female39.014.05.018.0StandardAnnual932.017.01.0
13.065.0Female49.01.010.08.0BasicMonthly557.06.01.0
24.055.0Female14.04.06.018.0BasicQuarterly185.03.01.0
35.058.0Male38.021.07.07.0StandardMonthly396.029.01.0
46.023.0Male32.020.05.08.0BasicMonthly617.020.01.0
58.051.0Male33.025.09.026.0PremiumAnnual129.08.01.0
69.058.0Female49.012.03.016.0StandardQuarterly821.024.01.0
710.055.0Female37.08.04.015.0PremiumAnnual445.030.01.0
811.039.0Male12.05.07.04.0StandardQuarterly969.013.01.0
912.064.0Female3.025.02.011.0StandardQuarterly415.029.01.0
CustomerIDidadesexotempo_como_clientefrequencia_usoligacoes_callcenterdias_atrasoassinaturaduracao_contratototal_gastomeses_ultima_interacaocancelou
881656449990.048.0Male11.027.01.018.0StandardAnnual618.285.00.0
881657449991.041.0Male46.025.03.02.0StandardQuarterly619.7915.00.0
881658449992.041.0Male27.020.02.012.0StandardQuarterly634.1727.00.0
881659449993.049.0Male37.023.04.016.0StandardAnnual666.6530.00.0
881660449994.045.0Male6.025.02.015.0BasicAnnual837.002.00.0
881661449995.042.0Male54.015.01.03.0PremiumAnnual716.388.00.0
881662449996.025.0Female8.013.01.020.0PremiumAnnual745.382.00.0
881663449997.026.0Male35.027.01.05.0StandardQuarterly977.319.00.0
881664449998.028.0Male55.014.02.00.0StandardQuarterly602.552.00.0
881665449999.031.0Male48.020.01.014.0PremiumQuarterly567.7721.00.0

Duplicate rows

Most frequently occurring

CustomerIDidadesexotempo_como_clientefrequencia_usoligacoes_callcenterdias_atrasoassinaturaduracao_contratototal_gastomeses_ultima_interacaocancelou# duplicates
02.030.0Female39.014.05.018.0StandardAnnual932.017.01.02
13.065.0Female49.01.010.08.0BasicMonthly557.06.01.02
24.055.0Female14.04.06.018.0BasicQuarterly185.03.01.02
35.058.0Male38.021.07.07.0StandardMonthly396.029.01.02
46.023.0Male32.020.05.08.0BasicMonthly617.020.01.02
58.051.0Male33.025.09.026.0PremiumAnnual129.08.01.02
69.058.0Female49.012.03.016.0StandardQuarterly821.024.01.02
710.055.0Female37.08.04.015.0PremiumAnnual445.030.01.02
811.039.0Male12.05.07.04.0StandardQuarterly969.013.01.02
912.064.0Female3.025.02.011.0StandardQuarterly415.029.01.02